Silveira et al. [1]} introduced reconfigurable HePIMs running four different BAs in their islands, namely, Genetic Algorithm (\({\mbox{\texttt {GA}}}\) ), double-point crossover GA (\({\mbox{\texttt {GAD}}}\) ), Differential Evolution (\({\mbox{\texttt {DE}}}\) ), and self-adjusting Particle Swarm Optimization (\({\mbox{\texttt {PSO}}}\) ) (see e.g., [2]}, [3]}, and [4]}). PIMs performance depends on a good calibration of the breeding cycle parameters (related to the involved BA) and, in addition to vital aspects of the parallel island architecture as island communication synchronism, island migration frequency, communication topology, and migration policy. We select two successful asynchronous HePIMs from [1]} maintaining their parameters and adding the reconfiguration frequency.
